基于RS_Adaboost的入侵检测方法

Intrusion detection method based on rough set and adaptive boost

针对入侵检测系统存在的对入侵事件高漏报率和误报率,提出了一种将粗糙集(RS)方法与自适应增强(Adaboost)算法相结合的入侵检测方法。利用粗糙集理论在处理大数据量、消除冗余信息等方面的优势,减少Adaboost训练数据,提高处理速度。Adaboost是一种构建准确分类器的学习算法,它将一族弱学习算法通过一定规则结合成为一个强学习算法,从而通过样本训练得到一个识别准确率理想的分类器。实验表明,该方法具有较高的检测率和检测效率。

To solve the problem of high rate of false negatives and false positives of IDS, an intrusion detection method was proposed in this paper, which combined Rough Set and Adaboost algorithm. Rough set was used to reduce amount of Adaboost＇ training data and improve running speed. Adaboost was a learning algorithm for constructing accurate classifiers. It can obtain a strong learning algorithm by combining a series of weak learning algorithms through some rules. The experimental results show that the model has high detection rate and detection efficiency.